Supervised Learning

In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to: build a course recommender system; analyze course related datasets, calculate cosine similarity, and create a similarity matrix; create recommendation systems by applying your knowledge of KNN, PCA, and [...]

In the first course of the Machine Learning Specialization, you will: build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn; build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic [...]

In the second course of the Machine Learning Specialization, you will: build and train a neural network with TensorFlow to perform multi-class classification; apply best practices for machine learning development so that your models generalize to data and tasks in the real world; build and use decision trees and [...]

The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These [...]

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will [...]

Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual [...]

In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods [...]

This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and [...]